Makeover Monday | Week 1 | 2019: NHL Attendance

And here we go again. Week 1 once more, now my third ‘week 1’ as a co-host of #MakeoverMonday.

I’m very excited about the year ahead, finding new datasets and dataviz challenges for our community.

Andy picked a dataset about NHL attendance, i.e. how many people go to different NHL games.

Here’s the original viz:

What works well:

Teams are sorted in alphabetical order.

Gridlines provide reference points.

Using black and red for increase and decrease.

What could be improved:

The axis is cut off which is a big No-No for bar charts, where length matters and helps us to compare. For example, Columbus looks to have half the attendance of Dallas – going by bar length – but it’s only a difference of 14,000 to 18,000 attendees, so Dallas has 28.6% more, not twice as much (100% more).

The labels are almost impossible to read.

The combination of black font on blue bars is not a good idea.

The bars seem to have a gradient from dark blue to light blue – why?

The 3D effect is not just unnecessary, it also makes it more difficult to understand the viz. It takes a second or third look to see where the centres of the little light blue squares are or the ends of the darker blue bars.

Why are the data points for the percentages (i.e. the light blue squares) connected with a line? This makes no sense as the data is categorical. The slope from Anaheim to Boston has no meaning whatsoever and shouldn’t be there. The percentages should only be represented as a line if they’re continuous data, like years for example.

My focus for this week:

In the interest of holidays, celebrating New Years and keeping things simple, my goal this week is to create a viz that is clean looking, easy to understand and communicates my key message.

I also want to focus on choosing suitable colors.

Learning something about NHL games, stadiums, attendance, etc. would also not be a bad thing 🙂

Thanks Andy for mentioning ‘there was a strike’ and for helping me understand attendance numbers.